| 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576 |
- """
- Metrics for data synthesis
- """
- import pandas as pd
- import numpy as np
- from sklearn.metrics import normalized_mutual_info_score, confusion_matrix
- def pairwise_mutual_information(frame: pd.DataFrame):
- """
- Return mutual information matrix for pairwise columns of a DataFrame.
- """
- columns = frame.columns.sort_values().to_list()
- mi = pd.DataFrame(columns=columns, index=columns, dtype=float)
- for row in columns:
- for col in columns:
- if pd.isnull(mi.at[col, row]):
- mi.at[row, col] = normalized_mutual_info_score(frame[row],
- frame[col])
- else:
- mi.at[row, col] = mi.at[col, row]
- return mi.round(3)
- def jensen_shannon_divergence(p, q, base=2):
- """
- Return the Jensen-Shannon divergence between two 1-D arrays.
- Parameters
- ---------
- p : array
- left probability array
- q : array
- right probability array
- base : numeric, default 2
- logarithm base
- Returns
- -------
- jsd : float
- divergence of p and q
- """
- # If the sum of probability array p or q does not equal to 1, then normalize
- p = np.asarray(p)
- q = np.asarray(q)
- p = p / np.sum(p, axis=0)
- q = q / np.sum(q, axis=0)
- from scipy.spatial.distance import jensenshannon
- return round(jensenshannon(p, q, base=base), 4)
- def error_rate(y_true, y_pred=None):
- """
- Return error (mis-classification) rate of one classifier result
- If there is only one parameter, it must be the confusion matrix;
- If there are two parameters, they must be true and predict labels;
- """
- if y_pred is None:
- if isinstance(y_true, pd.DataFrame):
- cm = y_true.values
- else:
- cm = y_true
- else:
- cm = confusion_matrix(y_true, y_pred)
- trace = np.trace(cm)
- sum_ = np.sum(cm)
- return round((sum_ - trace) / sum_, 4)
- def relative_error(x, y):
- """
- Return relative error of two variables: |x-y|/max(|x|, |y|)
- """
- m = np.maximum(np.abs(x), np.abs(y))
- return round(np.average(np.abs(x - y) / (m + 1e-6)), 4)
|
